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Abstract-Driving always involves risk. Various means have been proposed to reduce the risk. Critical motion detection of nearby moving vehicles is one of the important means of preventing accidents. In this paper, a computational model, which is referred to as the dynamic visual model (DVM), is proposed to detect critical motions of nearby vehicles while driving on a highway. The DVM is motivated by the human visual system and consists of three analyzers: 1) sensory analyzers, 2) perceptual analyzers, and 3) conceptual analyzers. In addition, a memory, which is called the episodic memory, is incorporated, through which a number of features of the system, including hierarchical processing, configurability, adaptive response, and selective attention, are realized. A series of experimental results with both single and multiple critical motions are demonstrated and show the feasibility of the proposed system.Index Terms-Assembly of adaptive-resonance-theory (ART) neural networks, driver-assistance system (DAS), dynamic visual model (DVM), fuzzy integral, spatiotemporal attention (STA) neural network.

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Sudden infant death syndrome (SIDS) is the major cause of death for infants aged one week to twelve months. The SIDS rate has declined owing to the awareness of caregivers and parents, but the rate is still high even in developed countries because of the difficulty in rescuing the infant immediately. Respiration, which can reflect various physiological conditions, is a basic but vital function for infants. Therefore, this study presents a respiration monitoring system with a video camera positioned in front of an infant to non-invasively detect the infant's respiratory frequency. The proposed system can continuously monitor the infant to detect unusual occurrences in the infant's respiration, to alert caregivers to attend to the infant immediately and reduce potential injuries from SIDS and other respiratory-related disease.The proposed system contains four major stages, including motion detection, candidate point extraction, respiration point selection, and respiratory frequency calculation. During motion detection the system captures images from video and decides whether to conduct the following stages. If no obvious motion is detected in the input frames, then SIDS may have occurred in the infant, and the system extracts candidate points by some spatial characteristics. Based on these points, the system then selects respiration points using a fuzzy integral technique with four temporal characteristics, including entropy, period, skewness, and kurtosis. Finally, the infant's respiratory frequency is calculated. Experimental data are obtained from ten infants, in 48 sequences with a total length of 150 minutes. The experimental results show that the proposed system is robust and efficient.Keywords-Home healthcare, vision-based infant monitoring system, vision-based infant respiratory frequency detection system, fuzzy integral.

In a visual driver-assistance system, road-sign detection and tracking is one of the major tasks. This study describes an approach to detecting and tracking road signs appearing in complex traffic scenes. In the detection phase, two neural networks are developed to extract color and shape features of traffic signs from the input scenes images. Traffic signs are then located in the images based on the extracted features. This process is primarily conceptualized in terms of fuzzy-set discipline. In the tracking phase, traffic signs located in the previous phase are tracked through image sequences using a Kalman filter. The experimental results demonstrate that the proposed method performs well in both detecting and tracking road signs present in complex scenes and in various weather and illumination conditions.